Assignments from Andrew Ng's Stanford ML course
contains code for completing a vectorized implementation of linear regression, with both gradient descent and the normal equation implemented to minimize the cost function and achieve ideal theta values
contains code for completing a vectorized implementation of a logistic regression classifier, with a regularized cost function
contains code for completing a vectorized implementation of an One-vs-All logistic regression classifier and neural networks to recognize hand-written digits
implement the backpropagation algorithm and regularization for neural networks, applying it to recognize hand-written digits
implement linear regression (w/ polynomial features added), using it to study models with different bias-variance properties from learning curves & what not
use SVMs to build a spam email classifier
implement K-means clustering algorithm to compress an image (finding 16 most common colors from RGB encoding and storing each pixel as an index of the 16); use PCA to find a low-dimensional representation of face images
create an anomaly detection algorithm, applying it to detect failing servers on a network; using collaborative filtering to build a recommender system for movies